Antipersonnel/Canister cartridges have been produced for 105 mm caliber and a number of gun calibers since cannons came into use. The basic principle is the expulsion of a large number of lethal sub-projectiles, fragments, flechettes, balls or other objects. The various sub-projectiles are accelerated during gun launch or during a detonation of an explosive charge to achieve a lethal velocity or the kinetic energy needed to accomplish suppression of troops, targets, or material obstacles. A distinction is typically made between “antipersonnel” cartridges, which implement an explosive fuze for payload dispersion, and “canister” cartridges, whose payloads spread via mechanical, aerodynamic, or inertial forces.
The dispersed payload needs to be evaluated in some manner to determine projectile performance. Various methods have been employed over the years in which actual projectiles were applied against full size targets, cloths, plywood, etc. Symmetrical patterns have been described using cone angles and basic measures of density. Statistical descriptions have also been used depending on the dispersion pattern type.
A typical target that is used in making such evaluations is a 10-man squad arranged in a “V” formation. A plan view of such a target 100 is shown in FIG. 1A. Typical distances 101 and 102 between adjacent squad members 110.N are 5 m each. FIG. 1B is an elevation view of an exemplary profile or silhouette for each squad member 110.N.
It is desirable to be able to evaluate the lethality of a canister projectile with varying pattern densities, multiple sub-projectile sizes, and pattern asymmetries.
A canister projectile has recently been developed for the 120 mm gun system. The pattern is currently being evaluated by describing the impacts on the target (such as that of FIGS. 1A and 1B) using a statistical curve fit from collected test data. This projectile design is amenable to a statistical curve fit approach because the pattern produced is of a uniform sub-projectile size, is symmetric, and has a single density distribution. The statistical curve fit is then utilized to determine the average number of impacts on a target at a given range. The average number of impacts on a target is then compared to incapacitation criteria to determine projectile performance.
Several problems arise, however, when using a statistical curve fit to describe the performance of a projectile having a particular dispersion pattern. The number of hits on each target silhouette is determined as a function of range based on the distribution fit to the data. As a result, the average number of hits is often a non-integer value with fractions of a hit (i.e. 2.4, 4.3, 5.6). In real life, however, it is not possible for a target silhouette to be hit with fractions of a sub-projectile. This makes it difficult to determine whether predetermined incapacitation criteria have been met where the model predicts a non-integer number of hits. Thus, for example, if a minimum of two hits is required for incapacitation, 1.99 hits could be deemed sufficient or insufficient depending on the degree of rounding-off involved.
An additional difficulty that arises is whether or not the pattern can be sufficiently described by a statistical curve fit. This often proves to be difficult especially if any asymmetries exist, multiple sub-projectile sizes are used, or varying density distributions are present.
In addition to the inherent difficulties in accurately describing patterns with statistical curve fits, the costs of conventional tests can also be significant. The 120 mm canister requires large cloth targets at various ranges to collect target data. This can lead to very expensive test setup costs due to the large size of the patterns and extensive ranges to be evaluated. The time associated with target setup and data collection is also quite costly. In some cases targets have grown to over 100′ long at certain ranges, and they only capture a portion of the pattern. Plywood silhouette targets are also currently used in conjunction with cloth targets for further validation, further complicating conventional test procedures.